Marta Arias

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Roni KhardonProfessor of Computer Science, Indiana University, BloomingtonVerified email at iu.edu
Ramon Ferrer i Cancho (0000-0002-7...Universitat Politecnica de Catalunya (RoR: 03mb6wj31). Barcelona, SpainVerified email at cs.upc.edu
Jose L. BalcazarProfessor, Computer Science, Universitat Politècnica de CatalunyaVerified email at upc.edu
Gail KaiserProfessor of Computer Science, Columbia UniversityVerified email at columbia.edu
Lenore J. CowenProfessor of Computer Science, Tufts UniversityVerified email at cs.tufts.edu
Ricard GavaldàUniversitat Politècnica de Catalunya + Amalfi AnalyticsVerified email at cs.upc.edu
Phil Grossformer PhD candidate, Columbia University, currently at GoogleVerified email at cs.columbia.edu
Albert BoulangerSenior Data Architect, Department of Neurology, Columbia UniversityVerified email at ldeo.columbia.edu
Rajmohan RajaramanProfessor of Computer Science, Northeastern UniversityVerified email at ccs.neu.edu
José C. RiquelmeUniversidad de SevillaVerified email at us.es
Joan Capdevila PujolUniversitat Politècnica de CatalunyaVerified email at ac.upc.edu
Chris H WigginsAssociate Professor, Columbia UniversityVerified email at columbia.edu
Steve LianoglouGenomic Data Scientist at largeVerified email at aratus.bio
Anshul KundajeAssociate Professor, Dept. of Genetics, Dept. of Computer Science, Stanford UniversityVerified email at stanford.edu
Christina LeslieMemorial Sloan Kettering Cancer CenterVerified email at mskcc.org
Cristina TirnaucaLecturer, Universidad de CantabriaVerified email at unican.es
Antti UkkonenSpeechly & University of HelsinkiVerified email at helsinki.fi
Philip Long
Adrià RecasensResearch Scientist, DeepMindVerified email at google.com

Marta Arias

Associate Professor, Universitat Politècnica de Catalunya

Verified email at lsi.upc.edu - Homepage

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Forecasting with twitter data M Arias, A Arratia, R Xuriguera ACM Transactions on Intelligent Systems and Technology (TIST) 5 (1), 1-24, 2014	286	2014
A deep-reinforcement learning approach for software-defined networking routing optimization G Stampa, M Arias, D Sánchez-Charles, V Muntés-Mulero, A Cabellos arXiv preprint arXiv:1709.07080, 2017	233	2017
An approach to software testing of machine learning applications C Murphy, GE Kaiser, M Arias	101	2007
Predicting electricity distribution feeder failures using machine learning susceptibility analysis P Gross, A Boulanger, M Arias, DL Waltz, PM Long, C Lawson, ... AAAI, 1705-1711, 2006	94	2006
Compact routing with name independence M Arias, LJ Cowen, KA Laing, R Rajaraman, O Taka Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and …, 2003	79	2003
GeoSRS: A hybrid social recommender system for geolocated data J Capdevila, M Arias, A Arratia Information Systems 57, 111-128, 2016	67	2016
Real-time ranking with concept drift using expert advice H Becker, M Arias Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007	45	2007
Challenging the generalization capabilities of graph neural networks for network modeling J Suárez-Varela, S Carol-Bosch, K Rusek, P Almasan, M Arias, ... Proceedings of the ACM SIGCOMM 2019 Conference Posters and Demos, 114-115, 2019	44	2019
System and method for grading electricity distribution network feeders susceptible to impending failure RN Anderson, A Boulanger, DL Waltz, P Long, M Arias, P Gross, H Becker, ... US Patent 7,945,524, 2011	44	2011
Learning Horn Expressions with LOGAN-H. M Arias, R Khardon, J Maloberti Journal of Machine Learning Research 8 (3), 2007	40	2007
Construction and learnability of canonical Horn formulas M Arias, JL Balcázar Machine Learning 85 (3), 273-297, 2011	35	2011
Learning closed Horn expressions M Arias, R Khardon Information and Computation 178 (1), 214-240, 2002	31	2002
Identifiability and transportability in dynamic causal networks G Blondel, M Arias, R Gavaldà International journal of data science and analytics 3 (2), 131-147, 2017	28	2017
Introduction to Network Dynamics M Arias, R Ferrer-i-Cancho, A Arratia	25	2020
Parameterizing random test data according to equivalence classes C Murphy, G Kaiser, M Arias Proceedings of the 2nd international workshop on Random testing: co-located …, 2007	24	2007
Learning regulatory programs that accurately predict differential expression with MEDUSA A Kundaje, S Lianoglou, X Li, D Quigley, M Arias, CH Wiggins, L Zhang, ... Annals of the New York Academy of Sciences 1115 (1), 178-202, 2007	20	2007
A multi-scale smoothing kernel for measuring time-series similarity A Troncoso, M Arias, JC Riquelme Neurocomputing 167, 8-17, 2015	16	2015
Canonical Horn representations and query learning M Arias, JL Balcázar International Conference on Algorithmic Learning Theory, 156-170, 2009	15	2009
From training to match performance: a predictive and explanatory study on novel tracking data J Fernández, D Medina, A Gómez, M Arias, R Gavalda 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW …, 2016	13	2016
A framework for quality assurance of machine learning applications C Murphy, GE Kaiser, M Arias	12	2006

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